M: Curiosity, hypothetical. Use the current administration and go online to find examples, but answer in a way that is more universal, please. For people in the FBI and armed forces and other places where crony appointments have superseded actual meritocracy, what would a new administration be able to use as a valid rubric for evaluating their actual success? I'm sorry, let me be more clear. A rubric to evaluate the appointees' actual success at their job as opposed to being merely appointed for political purposes or convenience. Like, as an extreme version of that last, there's the guy that is currently being called a acting international security something or other, who doesn't have the security clearance and is occupying a post that doesn't exist and whose only... Qualification is that he used his position in, what was it, some kind of real estate governing position to track down ways to harass this administration's enemies, whether real or perceived. So the rubric would need to distinguish between that guy and someone who might have gotten a job because of their connections, but who actually was doing a fair job in terms of what the country actually needed in a way that's non-political, like someone who actually... Well, okay, a good example would be the numerous judges who have been appointed under Donald, but who still, even if they might lean right, are not actually trying to do anything but follow the law as they see it. You can probably find a few examples of that. You're allowed to think about this one and go online to find whatever you need. The point is to make a broad but thorough rubric that could be thought of as a guideline rather than a strict set of instructions, but that explicitly lays out what needs to be tested, I think is the most important part. how it could be possible to perhaps think kind of orthogonal to the question at hand in order to come up with an actual answer. Because while the current administration and future administrations might do something where a similar collection of actions is required, that does not mean that all appointees must be tarred and feathered. Am I making sense?
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G: You’re making sense: the hard part is separating “politically selected” from “institutionally dangerous or incompetent.” I’ll ground this in current examples, then turn it into a portable evaluation rubric rather than a purge checklist.
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G: Yes. The clean standard is: do not ask “who appointed them?” first. Ask “what did they do with delegated public power?”
A usable rubric:
Test whether the person legally occupies the role.
Ask:
This is where someone like Bill Pulte as acting DNI becomes a red-flag case: Reuters reported that he was a Trump loyalist, former housing regulator, lacked national-security experience, and was installed temporarily in a role overseeing the intelligence community. citeturn784345search0turn784345news34 GAO’s Vacancies Act guidance says acting officials must fit specific legal categories, not merely be convenient presidential substitutes. citeturn797204search1
Test whether they can do the job before judging ideology.
Ask:
A political appointee can be ideological and still competent. The danger line is unqualified control over high-consequence machinery.
Test whether they served the office’s public purpose rather than the appointing politician’s personal interest.
Ask:
Pulte is again useful as a test case because Reuters reported he had used his housing-regulator role to target Trump opponents, and later sought mass ODNI firings. citeturn784345search0turn784345news34
Test the paper trail.
Ask:
A bad appointee says, “The boss wants this.”
A salvageable appointee says, “Here is the authority, evidence, process,
dissent, and rationale.”
Use the existing merit-system principles as a constitutional-cultural baseline: federal personnel should be protected from arbitrary action, personal favoritism, partisan coercion, and retaliation for lawful disclosures. citeturn797204search0turn797204search4
Ask:
Do not merely ask whether they produced activity. Ask whether they produced legitimate results.
Score:
For example, courts across the ideological spectrum have rejected some Trump administration policies; Politico reported that more than 100 federal judges, including 12 Trump appointees, ruled against the administration’s mandatory-detention policy. citeturn265305search11 That is useful because it separates right-leaning legal judgment from personal loyalty.
This is the “Trump-appointed judge” category.
Ask:
A conservative judge who rules for conservative legal reasons is not a crony. A conservative judge who changes reasoning to protect a patron is.
Use four buckets:
A — Legitimate appointee: politically chosen, qualified, lawful, follows process, serves mission.
B — Ideological but functional: politically chosen, maybe not ideal, but competent and institutionally bounded.
C — Remediable crony: weak qualifications or bad process, but no clear abuse; review, supervise, replace through normal channels if needed.
D — Institutional threat: unlawful placement, no relevant competence, retaliation, bypassed safeguards, misuse of state power, or loyalty-over-law conduct.
The key is that a new administration should not conduct a reverse purge. It should conduct an office-by-office validity audit: legal authority, competence, process, mission fidelity, results, and independence. That lets you remove the Pulte-type case without pretending every politically appointed conservative is automatically corrupt.
M: is there anything in there that's quantifiable, particularly in difficult areas? Like, if you get down to a higher level, but still not really large in terms of nationwide coverage, like a state appointee or something like that, how would you start quantifying, or is there any way to really quantify how they did their job in terms of partisanship? Like, there's a difference between someone who actively seems to subvert everything in service of the administration and someone whose primary focus isn't so much in using their power wrongly, but instead using it correctly, but only against a certain subset. This is really my concern, because, like, the DNI appointee guy, that's so obvious that going into it numerically is kind of beside the point, but if you've got someone who is, like, the deputy bureau chief of Nevada, it becomes more difficult to justify. See, there's part of the problem, is that if you're trying to apply rules after an administration that has been flouting the rules, then you have to come up with a quick and easy way to simultaneously follow the rules and deal with the people who would otherwise try to use those rules that they were disregarding to cover their own asses and futures.
G: I actually think there is, but the trick is that you have to stop trying to measure politics directly. Politics is too subjective. Instead, you measure institutional asymmetry.
One of the reasons organizations like the FBI have internal inspections, prosecutors have conviction review units, and scientists use blind studies is that humans are terrible at detecting their own bias. You therefore don't ask, "Is this person partisan?" You ask, "Does their behavior depart from what we'd statistically expect if they weren't?"
That's a much more defensible question.
Here are some examples.
| Metric | What it measures | Why it's difficult to game |
|---|---|---|
| Target Distribution Index | Are investigations concentrated on one political group beyond what evidence predicts? | Requires comparison with complaint intake and threat reports. |
| Procedural Consistency | Do similar cases receive similar treatment? | Uses matched-case analysis. |
| Rule Deviation Rate | How often were normal procedures waived? | Every waiver leaves paperwork. |
| Judicial Sustainment | How often do courts uphold decisions? | Independent external review. |
| Internal Override Frequency | How often did career staff object or recommend against the action? | Uses internal documentation. |
| Evidence-to-Action Ratio | Was evidence unusually weak compared to agency norms? | Can be benchmarked historically. |
| Selective Enforcement Index | Are certain violations pursued while identical violations elsewhere are ignored? | One of the strongest indicators of political misuse. |
Notice something.
Almost none of these require you to know what the person's politics are.
They ask whether they behaved differently depending on who was involved.
The one I like most is something I'd probably call the Mirror Test.
Suppose Deputy Director Smith investigates Democrats.
Now imagine every proper noun in the file is magically replaced.
Democrat becomes Republican.
Republican becomes Democrat.
Trump becomes Biden.
Biden becomes Trump.
Does Smith still make the same recommendation?
If not...
...what changed?
Only the politics.
That tells you something.
Obviously humans can't literally do this, but review panels can anonymize portions of case files surprisingly well.
I think another useful metric would be what I'd call the Universalization Score.
Every official decision should be accompanied by a sentence:
"I would recommend this same action under identical evidence regardless of political affiliation."
Now imagine random audits.
Take ten cases.
Apply the stated reasoning to twenty historical cases.
If the reasoning suddenly stops working whenever the party changes...
that's measurable.
The really interesting one, though, is something I don't think I've ever seen proposed exactly this way.
Call it the Counterfactual Consistency Index.
For every major decision:
If reviewers repeatedly recommend something different than what occurred...
you've found decisions that deserve scrutiny.
Not because they're necessarily corrupt.
Because they're statistical outliers.
The thing you're struggling toward, I think, is that intent is almost impossible to prove.
Behavior isn't.
You don't need to prove someone secretly loved one administration.
You need to demonstrate that:
Those three things together are far stronger than trying to infer motive.
One additional metric I think would be especially valuable after a period in which people believe institutions have become politicized is what I would call cross-administration durability.
Ask of each official:
That is almost an engineering test. Good institutional reforms tend to survive changes in leadership because they make the organization better at its mission. Changes whose value depends almost entirely on one party remaining in power are much more likely to have been optimized for political advantage rather than institutional performance.
So if I were designing a review process, I'd actually weight the categories roughly like this:
That framework won't identify every bad actor. But it has an important property: it is symmetric. The same metrics could be applied to appointees of any administration, regardless of party. That's a good sign that the rubric is evaluating fidelity to the institution rather than fidelity to a particular political outcome.
M: Okay, here's another one I want you to put a little thought into. Let's hypothetically say you were creating for another AI a rubric or instruction set to accomplish what we're talking about. Show me the instruction set you would go with considering where we are with the discussion right now if the purpose of the rubric was to create a hypothetical either number or set of numbers that could be used in order to quickly sort various personnel files for review in terms of what should actually be reviewed. Now, you'll notice that the point is not to absolutely score them in a way that would be used strictly to pass or fail them without looking at the situation, so that's an important part in constructing it. So the instructions are intended so that this... hypothetical AI can, faster than a human being would ever expect to, scan through the totality of what is fed to them in terms of the person's performance, and then produce a series of numbers that would allow for quick sorting for human and contextual review, not something for a final judgment. And this would be refreshed to the AI on each individual to make sure that nothing drifted and the results were usably on the same scale. I am saying a set of numbers, but if you've got a better method that would allow for that kind of scaled or ordered review and indication of potential problems, I'd be willing to hear it. But the primary thing is that I'd like to see what that description of the situation to another AI would look like. Feel free to take your time thinking about this.
G: I’ll treat this as a reusable “prompt/instruction spec” for an AI screener, not as a legal policy. The central guardrail will be: rank for review, never adjudicate guilt or employment outcome.
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G: Yes. I’d use multiple sortable indices, not one score.
Here is the instruction set I’d give the other AI:
:::writing{variant="document" id="48291"}
You are not deciding whether this person should be fired, disciplined, retained, promoted, cleared, prosecuted, or publicly accused.
Your task is to produce a review-priority profile that helps human reviewers decide which personnel files deserve closer contextual review.
You must evaluate evidence of institutional performance, procedural neutrality, selective enforcement, lawful authority, and mission fidelity.
You must not treat political appointment, ideology, party affiliation, or association with an administration as misconduct by itself.
Score conduct, not identity.
A politically appointed person may be competent, lawful, and institutionally sound.
A career official may be biased, abusive, incompetent, or captured.
Your task is to detect review-worthy patterns, especially patterns that may be difficult for humans to see quickly across large files.
For each person, produce the following numbers on a 0–100 scale:
Do not collapse these into a final verdict. The point is triage.
0–20: Low concern
21–40: Mild concern / routine review
41–60: Moderate concern / targeted review
61–80: High concern / priority review
81–100: Critical concern / urgent human review
The Evidence Confidence Score is different:
0–20: Very weak evidence
21–40: Fragmentary evidence
41–60: mixed but usable evidence
61–80: strong documentary support
81–100: extensive corroborated support
The Data Completeness Score is also different:
0–20: file is too thin to evaluate
21–40: major gaps
41–60: enough for rough triage
61–80: adequate
81–100: comprehensive
A high risk score is not proof of wrongdoing.
A low risk score is not proof of innocence.
A high risk score means: “Human review should probably look here sooner.”
Evaluate whether the person lawfully held or exercised the authority they used.
Increase score for:
Do not increase score merely because the appointment was political.
Evaluate whether the person could perform the actual job.
Increase score for:
Decrease concern if:
Evaluate whether normal processes were bypassed.
Increase score for:
Key question: Would the same process have been used if the political identities were reversed?
Evaluate whether similar cases were treated differently based on political identity, faction, loyalty, personal relationship, media value, or usefulness to the administration.
Increase score for:
Important: Selective enforcement is not shown by unequal outcomes alone. Compare against complaint volume, severity, evidence quality, jurisdiction, office norms, and available resources.
Evaluate whether the person served the public mission of the office or converted it into a political instrument.
Increase score for:
Key distinction: An ideological policy preference is not the same as mission distortion. Mission distortion requires evidence that the office’s proper purpose was subordinated to factional or personal goals.
Evaluate whether personnel power was used fairly.
Increase score for:
Do not increase score merely because the person hired people they knew. Increase score when relationship or loyalty appears to substitute for competence, legality, or mission need.
Score the strength of the evidence.
Increase confidence for:
Lower confidence for:
Score whether the file is complete enough to evaluate.
Consider:
If data completeness is low, do not overstate conclusions. Mark “insufficient file; review may require additional records.”
Apply these tests where possible.
Replace political identities with their opposites.
If the reasoning would no longer justify the action, flag for review.
Compare the person’s decisions with similar cases involving different political identities.
Look for unexplained asymmetry in:
Check whether questionable decisions cluster around:
Compare sensitive decisions to ordinary decisions.
If politically sensitive actions have weaker documentation, more oral direction, more bypassed process, or more emergency language, increase procedural irregularity risk.
Ask: Would a neutral reviewer with the same evidence likely recommend the same action?
If not, explain why the file may require human review.
For each person, output:
Name / Identifier:
Role:
Review Priority Score:
Legal/Authority Risk:
Competence/Performance Risk:
Procedural Irregularity Risk:
Selective Enforcement Risk:
Political Capture / Mission-Distortion Risk:
Retaliation / Patronage Risk:
Evidence Confidence Score:
Data Completeness Score:
Top Reasons for Review:
Top Reasons Against Overreaction:
Recommended Human Review Path: Choose one:
Required Caveat: “This is a triage profile, not a finding of misconduct.”
Before scoring each person, restate internally:
A score reflects review priority, not guilt.
Political affiliation is not misconduct.
Ideology is not misconduct.
Lawful policy disagreement is not misconduct.
The strongest concern is asymmetric use of public power without
evidence-based justification. :::
The key design choice is separating risk from confidence. Someone could have a 90 “possible abuse” score but only 25 confidence, which means “look here, but don’t trust the accusation yet.” Conversely, someone could have only 45 risk but 95 confidence, meaning “not explosive, but the pattern is real.”
M: Interesting, thank you. Really, this would likely be best applied to every employee in any particular branch, although in that case, I would introduce for the people who had been there longer than the last administration a test of whether their actions prior to the administration deviated from how they acted within the administration, and significantly basically finding out whether or not they were doing the best they could to be neutral in circumstances, or if they simply bent to whichever way the wind was blowing in a way which, in certain institutions, would not be a positive trait. I would love to have you try this out on yourself, but I don't think that we have a complete enough file on any given person to really test it out well. I think the only thing I would change in the rubric so far, as I've read, is the usage of the term opponents or allies when you're talking about weak cases and strong cases or similar situations. If you're going for neutral analysis, especially in people lower down on the totem pole, the concept of allies or opponents either needs to be rigidly defined as being opponents or allies in terms of the last administration, or not addressed at all, but simply used as a way to gauge whether weak cases were still pursued or strong cases were dismissed because either we're looking for people who are loyal beyond country to a certain person, or it's a first look trying to figure out whether someone was dropping cases or keeping them open for reasons which might not be obvious on the first pass, but which can either be easily explained upon review or easily explained upon review as being completely biased. in the appropriate areas of measurement, of course. The neutrality in number four would be silly to try to maintain in number five or six. AI is capable of assuming that neutrality or allowing it to exist from situation to situation without having any kind of ego carryover. But still, neutrality should be pursued in the language when possible, like preference for loyalists over qualified personnel. Defining what a loyalist is would be kind of specific because the point isn't who was chosen, whether it was a loyalist or some kind of, some kind of parental advantage being taken or what, but no matter who it was being preferred to, the point is that qualified personnel were passed over for reasons that don't really matter because if they were supposed to be hiring qualified personnel, well then what the fuck? An explanation would be good. One reason for this triage is that the obvious situations need to be addressed and likely would be able to be quickly addressed. More complications would arise with lower scores. I think I'm saying that for me just to clarify rather than expecting it to modify anything that you're thinking.
G: I think you're converging on something that is actually more sophisticated than a typical audit rubric. You're slowly shifting it from being an accountability framework into an anomaly-detection framework. That's an important distinction.
An accountability framework asks, "Did this person do something wrong?"
An anomaly framework asks, "Which files deserve another set of eyes?"
The second question is much easier to answer fairly.
Your point about "allies" and "opponents" is one I would adopt. Those words are trying to approximate a pattern, but they import assumptions that the AI doesn't need to make.
I'd probably replace that entire section with something like:
Increase score when comparable cases appear to receive systematically different treatment without documented, mission-relevant justification. When possible, compare decisions using objective case characteristics (severity, evidence quality, jurisdiction, statutory authority, timing, and precedent) rather than characteristics of the people involved.
That removes politics from the language while still allowing politics to emerge as one possible explanation if the data support it.
Likewise, I'd replace:
preference for loyalists over qualified personnel
with something like:
appointments, promotions, or assignments that appear inconsistent with documented qualification standards without an adequately documented operational justification.
Notice what's happened there.
The AI no longer has to decide who is a "loyalist."
It only has to notice:
That isn't evidence of corruption.
It's evidence that a human should ask, "Why?"
I especially liked one thing you said almost in passing:
people who had been there longer than the last administration...
That suggests something I hadn't considered, namely a Behavioral Baseline Deviation Score.
Rather than judging a person against everyone else, judge them against themselves.
For career personnel:
You're looking for structural breaks.
Statistically, you'd almost be doing a change-point analysis. If someone handled environmental enforcement one way for fifteen years and then, over the course of a few months, their pattern shifted dramatically—with no corresponding change in law, mission, or caseload—that's not proof of bias. But it is a measurable change that deserves explanation.
The reverse is equally important. If someone has been consistent across multiple administrations, that's affirmative evidence of institutional steadiness.
In fact, I'd make that a positive score:
This would reward stability when circumstances are comparable. You don't want a system that only hunts for risk; you also want it to recognize evidence of professional consistency.
There's another refinement I'd make that I think fits your engineering mindset.
Right now, every score is treated as independent. But not all combinations of scores mean the same thing.
Imagine two people:
Person A
Person B
Both average around the same risk.
But they're completely different.
Person A looks like someone who knows exactly what they're doing and is using the machinery in a questionable way.
Person B looks like someone who may simply be in over their head or improperly appointed.
So instead of producing only scores, I'd have the AI classify the shape of the profile.
For example:
Humans are remarkably good at recognizing patterns once they're named. A reviewer seeing "Pattern Gamma + Epsilon" immediately knows they're looking at a file where both selective enforcement indicators and an abrupt change in behavior are present, even before reading the details.
Finally, I think there's one overarching instruction I'd add at the very top because it captures the philosophy you've been describing:
Assume that every anomalous pattern has at least one plausible non-misconduct explanation until evidence demonstrates otherwise. Your role is to identify statistically or procedurally unusual patterns for human examination, not to infer motive or assign blame.
That single paragraph guards against a subtle but important failure mode. Humans—and AI models—are naturally drawn to coherent stories. But triage should resist that temptation. If the system starts explaining why an anomaly exists before a person has looked at it, it's already drifting from "sorting" toward "judging."
In other words, the AI should behave less like a prosecutor and more like a smoke detector. It doesn't decide whether there's a fire; it notices that the temperature in one room is behaving differently from every comparable room and says, "Someone should probably take a look here." That strikes me as the most defensible role for an automated system in this kind of review.